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A Framework for Implementing Metaheuristic Algorithms Using Intercellular Communication
Yerko Ortiz1, Javier Carrión1, Rafael Lahoz-Beltrá2
1School of Informatics and Telecommunications, Faculty of Engineering and Sciences, Diego Portales University, Santiago, Chile.
Frontiers in Bioengineering and Biotechnology
|May 27, 2021
Summary
This study introduces a framework for implementing Artificial Intelligence (AI) metaheuristics (MH) using synthetic biology in cell colonies. This approach leverages cell parallelism to accelerate complex problem-solving, demonstrating faster execution for algorithms like genetic algorithms.
Area of Science:
- Computational Biology
- Artificial Intelligence
- Synthetic Biology
Background:
- Metaheuristics (MH) are AI methods for solving complex problems but are computationally intensive due to large solution space exploration.
- Existing MH approaches require significant computational resources, limiting their application in certain scenarios.
Purpose of the Study:
- To establish general mappings for implementing MH using synthetic biology constructs within cell colonies.
- To harness the inherent parallelism of cell colonies for computationally demanding AI tasks.
- To utilize natural cellular processes for implementing computational dynamics of MH.
Main Methods:
- Proposed a framework mapping MH elements to synthetic circuits in growing cell colonies.
- Utilized cell-cell communication (quorum sensing, conjugation) and environmental signals as evolution operators.
- Developed automated MH simulation generators for the 'gro' simulator and implemented Simple Genetic Algorithms and Simulated Annealing as synthetic circuits.
Main Results:
- Demonstrated automatic production of synthetic counterparts that mimic MH behavior.
- Showcased accelerated execution speeds in terms of generations due to cell colony parallelism.
- Extended the framework to incorporate other computational models, exemplified by Cellular Automata.
Conclusions:
- The synthetic biology framework effectively replicates MH behavior in cell colonies.
- Cell colony parallelism offers a significant speedup for MH execution.
- The framework is adaptable, supporting the implementation of diverse computational models beyond MH.
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